import os
import numpy as np
import pandas as pd
import seaborn as sns
from haversine import haversine

os.chdir(os.path.dirname(__file__))

deploy = pd.read_csv('../../data/deployments.csv')
pickup = pd.read_csv('../../data/pickups.csv')
rides  = pd.read_csv('../../data/rides.csv')

deploy.info()
deploy.describe()
deploy.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 17969 entries, 0 to 17968
Data columns (total 4 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   task_id             17969 non-null  object
 1   vehicle_id          17969 non-null  object
 2   time_task_created   17969 non-null  object
 3   time_task_resolved  17969 non-null  object
dtypes: object(4)
memory usage: 561.7+ KB

	task_id	vehicle_id	time_task_created	time_task_resolved
count	17969	17969	17969	17969
unique	17969	3920	17966	17967
top	JBMHRfpo7R8Obb27t7E6	IFE0zdS4rkTO4Xj5EZSM	2019-05-11 09:20:51.366 UTC	2019-05-24 13:50:32.87 UTC
freq	1	19	2	2

	task_id	vehicle_id	time_task_created	time_task_resolved
0	6fy0fO6BHW2IvxunS54N	i1Fycyzyi1HeeEN8eVh1	2019-05-19 02:34:31.999 UTC	2019-05-21 04:52:29.113 UTC
1	36AUD64tmGggMdmXLwY3	inxamrBymPp1kKuGoOXE	2019-05-19 03:20:30.842 UTC	2019-05-20 06:38:49.76 UTC
2	TcPf6gwxm98aVufsLcCo	1DfPwD1x6teNIhv8aS3p	2019-05-19 00:39:49.472 UTC	2019-05-19 05:13:56.514 UTC
3	MiHEvPS2yD9UKkcbTLUN	1TkCdPxuKYSg68Ongllz	2019-05-19 04:28:55.896 UTC	2019-05-20 07:00:09.397 UTC
4	wpqaaO3EilBcF943xJYd	STyaEsJk2oiFz1NEJHE7	2019-05-19 06:20:23.947 UTC	2019-05-20 07:55:36.504 UTC

pickup.info()
pickup.describe()
pickup.head()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 18089 entries, 0 to 18088
Data columns (total 5 columns):
 #   Column              Non-Null Count  Dtype 
---  ------              --------------  ----- 
 0   task_id             18089 non-null  object
 1   vehicle_id          18089 non-null  object
 2   qr_code             18089 non-null  object
 3   time_task_created   18089 non-null  object
 4   time_task_resolved  18089 non-null  object
dtypes: object(5)
memory usage: 706.7+ KB

	task_id	vehicle_id	qr_code	time_task_created	time_task_resolved
count	18089	18089	18089	18089	18089
unique	18089	4196	4196	18089	18089
top	4Sa7B6f5htctNTfgkS9j	ZS08r6re6o1yJ3QuCAei	TVEWB7	2019-05-01 20:31:23.999 UTC	2019-05-18 09:13:53.242 UTC
freq	1	19	19	1	1

	task_id	vehicle_id	qr_code	time_task_created	time_task_resolved
0	yV4kXBtAx6GNRcJDLsF2	JkDkmUHoZ4ngg7hAx0F4	XXBGC5	2019-05-04 21:30:07.089 UTC	2019-05-04 21:30:07.883 UTC
1	xGAnYaxqoyGUa8RFcaTr	NpbLgU80Rliumo1QkoeZ	NWM2QA	2019-05-04 00:07:17.13 UTC	2019-05-05 16:11:24.233 UTC
2	MdkXjdQEW0BfFssY1w44	qHbfAktQ2MYfme75EhBM	M43B0N	2019-05-04 18:36:10.529 UTC	2019-05-04 18:36:10.998 UTC
3	p23c48p07N5fU3e4HOdx	mcRXxmiFF6HrxJ5is2a1	ZC85M1	2019-05-04 18:09:22.712 UTC	2019-05-04 18:09:23.061 UTC
4	XfJqWe0fnFeHxB44T2wG	jG0LWBJM87zpBajNWeS7	Z84V6J	2019-05-04 16:42:35.639 UTC	2019-05-04 16:50:26.062 UTC

rides.info()
rides.describe()
rides.head()
rides.apply(pd.Series.nunique)

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 79042 entries, 0 to 79041
Data columns (total 9 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   ride_id          79042 non-null  object 
 1   vehicle_id       79042 non-null  object 
 2   time_ride_start  79042 non-null  object 
 3   time_ride_end    79042 non-null  object 
 4   start_lat        79042 non-null  float64
 5   start_lng        79042 non-null  float64
 6   end_lat          79042 non-null  float64
 7   end_lng          79042 non-null  float64
 8   gross_amount     78971 non-null  float64
dtypes: float64(5), object(4)
memory usage: 5.4+ MB

	start_lat	start_lng	end_lat	end_lng	gross_amount
count	79,042.00	79,042.00	79,042.00	79,042.00	78,971.00
mean	48.86	2.32	48.85	2.32	3.36
std	0.02	0.03	0.55	0.04	2.43
min	48.61	2.02	0.00	0.00	1.00
25%	48.85	2.30	48.85	2.30	1.81
50%	48.86	2.32	48.86	2.32	2.60
75%	48.87	2.34	48.87	2.35	4.02
max	49.06	2.55	49.06	2.55	80.93

	ride_id	vehicle_id	time_ride_start	time_ride_end	start_lat	start_lng	end_lat	end_lng	gross_amount
0	dVPK7fEkGfd3HJiYRMkj	f42vVrTOJh7yVYJACDCv	2019-05-02 19:24:01.692 UTC	2019-05-02 19:30:43.433 UTC	48.83	2.38	48.83	2.36	2.00
1	y9xR90cTsRdWkC2JuO0r	ZWuRqR3eNjRVEQlHPsJy	2019-05-02 12:32:39.928 UTC	2019-05-02 12:52:42.333 UTC	48.84	2.30	48.83	2.33	4.00
2	GSX8MZkpWEttApiFsELL	j8JnGWoM5bohbxAOzm42	2019-05-02 21:59:50.218 UTC	2019-05-02 22:08:13.39 UTC	48.89	2.29	48.89	2.31	2.25
3	l5npy2l9IyXmaCHCbcSu	ghh4d9VJGjTMxgW54etd	2019-05-02 15:16:37.857 UTC	2019-05-02 15:26:40.932 UTC	48.84	2.27	48.85	2.28	2.50
4	rpCjuQyIaYd4Ox1wu7Pp	yiQ3vmhhMGLr0hiws6Kq	2019-05-02 16:22:29.716 UTC	2019-05-02 16:49:16.348 UTC	48.84	2.35	48.84	2.32	5.01

ride_id            79042
vehicle_id          3862
time_ride_start    79038
time_ride_end      79040
start_lat          47516
start_lng          59486
end_lat            48914
end_lng            60728
gross_amount        1636
dtype: int64

events = (pd
    .concat([
        (rides
            .assign(
                kms=lambda df: df.apply(lambda row:
                    haversine((row['start_lat'], row['start_lng']), (row['end_lat'], row['end_lng'])), 1),
                mins=lambda df:
                    (
                      pd.to_datetime(rides.time_ride_end, format="%Y-%m-%d %H:%M:%S.%f %Z", errors='coerce')
                    - pd.to_datetime(rides.time_ride_start,   format="%Y-%m-%d %H:%M:%S.%f %Z", errors='coerce')
                    ).dt.seconds / 60
            )
            .set_index(['ride_id', 'vehicle_id', 'gross_amount', 'kms', 'mins'])[['time_ride_start', 'time_ride_end']].stack()
            .reset_index().rename(columns={'level_5':'event', 0:'ts'})
            .replace({'time_ride_start': 'ride_start', 'time_ride_end': 'ride_end'})
        ),
        (pickup
            .set_index(['task_id', 'vehicle_id'])[['time_task_created', 'time_task_resolved']].stack()
            .reset_index().rename(columns={'task_id': 'pickup_id', 'level_2': 'event', 0:'ts'})
            .replace({'time_task_created': 'pickup_start', 'time_task_resolved': 'pickup_end'})
        ),
        (deploy
            .set_index(['task_id', 'vehicle_id'])[['time_task_created', 'time_task_resolved']].stack()
            .reset_index().rename(columns={'task_id': 'deploy_id', 'level_2': 'event', 0:'ts'})
            .replace({'time_task_created': 'deploy_start', 'time_task_resolved': 'deploy_end'})
        )
    ])
    [['vehicle_id', 'deploy_id', 'pickup_id', 'ride_id', 'gross_amount', 'kms', 'mins', 'event', 'ts']]
    .sort_values(['vehicle_id', 'ts'])
    .reset_index(drop=True)
    # `status`-column to show the latest service event, eg. `deploy_end` means it's available on the street.
    .assign(status=lambda df: df.event.where(~df.event.str.contains('ride')))
    .assign(status=lambda df: df.groupby('vehicle_id').status.ffill())
)
events.head()

	vehicle_id	deploy_id	pickup_id	ride_id	gross_amount	kms	mins	event	ts	status
0	00TW9JELAItFazRxej7f	4VcfrC79JKgkXK6p3kNJ	NaN	NaN	NaN	NaN	NaN	deploy_start	2019-04-27 10:44:42.154 UTC	deploy_start
1	00TW9JELAItFazRxej7f	4VcfrC79JKgkXK6p3kNJ	NaN	NaN	NaN	NaN	NaN	deploy_end	2019-05-01 05:45:29.133 UTC	deploy_end
2	00TW9JELAItFazRxej7f	NaN	NaN	yqe0ImYkRmrcmdYzfLf9	1.22	0.20	1.47	ride_start	2019-05-01 12:18:03.616 UTC	deploy_end
3	00TW9JELAItFazRxej7f	NaN	NaN	yqe0ImYkRmrcmdYzfLf9	1.22	0.20	1.47	ride_end	2019-05-01 12:19:31.701 UTC	deploy_end
4	00TW9JELAItFazRxej7f	NaN	NaN	8Fn4pWtZh6oAcvDzBeaX	2.93	1.01	12.87	ride_start	2019-05-01 14:37:06.089 UTC	deploy_end

Check assumptions

Invalid rides

offending_rides = events[events.ride_id.notnull() & ~(events.status == 'deploy_end') & events.status.notnull()]
offending_rides.groupby(['event', 'status']).size()
offending_rides.describe()

event       status      
ride_end    pickup_end       311
            pickup_start    6421
ride_start  pickup_end       384
dtype: int64

	gross_amount	kms	mins
count	7,083.00	7,116.00	7,108.00
mean	4.77	3.54	25.05
std	3.21	91.12	21.25
min	1.00	0.00	0.03
25%	2.63	0.76	10.90
50%	3.88	1.67	19.18
75%	5.88	2.95	32.43
max	54.64	5,439.30	357.62

Quite some rides ending after a service pickup.

Other discrepancies possibly due to both eventual consistency in the source data, and invalid rides as well.

Lack of gross_amount?

no_amount = events[events.gross_amount.isnull() & events.ride_id.notnull()]
no_amount.info()
no_amount.describe()
no_amount.ride_id.nunique(), offending_rides.ride_id.nunique(), len(set(no_amount.ride_id) & set(offending_rides.ride_id))

<class 'pandas.core.frame.DataFrame'>
Int64Index: 142 entries, 980 to 230062
Data columns (total 10 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   vehicle_id    142 non-null    object 
 1   deploy_id     0 non-null      object 
 2   pickup_id     0 non-null      object 
 3   ride_id       142 non-null    object 
 4   gross_amount  0 non-null      float64
 5   kms           142 non-null    float64
 6   mins          142 non-null    float64
 7   event         142 non-null    object 
 8   ts            142 non-null    object 
 9   status        142 non-null    object 
dtypes: float64(3), object(7)
memory usage: 12.2+ KB

	gross_amount	kms	mins
count	0.00	142.00	142.00
mean	NaN	2.63	87.58
std	NaN	2.30	26.56
min	NaN	0.01	50.23
25%	NaN	0.81	67.18
50%	NaN	2.13	82.70
75%	NaN	3.94	102.56
max	NaN	9.36	190.53

(71, 6732, 33)

Half of rides without an amount is also and offending ride. Lack of amount is unrelated to distance.

Pricing

sns.scatterplot(
    data=(events
        [events.kms < 20]
        .drop_duplicates(subset=['ride_id'])
    ),
    x='gross_amount',
    y='kms'
)

<AxesSubplot:xlabel='gross_amount', ylabel='kms'>

sns.scatterplot(
    data=(events
        [events.kms < 20]
        .drop_duplicates(subset=['ride_id'])
    ),
    x='gross_amount',
    y='mins'
)

<AxesSubplot:xlabel='gross_amount', ylabel='mins'>

QR-codes

(pickup.groupby(['vehicle_id']).qr_code.nunique() == 1).all()

True

QR-codes map to vehicle IDs 1:1.

Prep data quality checks

events.select_dtypes('object').apply(lambda c: c.dropna().str.len()).describe()
pickup.qr_code.dropna().map(len).describe()

	vehicle_id	deploy_id	pickup_id	ride_id	event	ts	status
count	230,200.00	35,938.00	36,178.00	158,084.00	230,200.00	230,200.00	226,104.00
mean	20.00	20.00	20.00	20.00	9.63	26.90	10.38
std	0.00	0.00	0.00	0.00	1.36	0.35	0.78
min	20.00	20.00	20.00	20.00	8.00	23.00	10.00
25%	20.00	20.00	20.00	20.00	8.00	27.00	10.00
50%	20.00	20.00	20.00	20.00	10.00	27.00	10.00
75%	20.00	20.00	20.00	20.00	10.00	27.00	10.00
max	20.00	20.00	20.00	20.00	12.00	27.00	12.00

count              18,089.00
mean                    6.00
std                     0.00
min                     6.00
25%                     6.00
50%                     6.00
75%                     6.00
max                     6.00
Name: qr_code, dtype: float64

All id-columns are made of 20 character-long strings. QR-code column made of 6 characters.